1. Introduction to Forecasting. 1.1 The Nature and uses of Forecasts. 1.2 Some Examples Time Series. 1.3 Forecasting Process. 1.4 Resources for 2. Statistics Background 2.1 Introduction. 2.2 Graphical Displays. 2.3 Numerical Description Series Data. 2.4 Use Data Transformations Adjustments. 2.5 General Approach Analysis 2.6 Evaluating Monitoring Model Performance. 3. Regression 3.1 3.2 Least Squares Estimation in Linear Models. 3.3 Statistical Inference Regression. 3.4 Prediction New...

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy high-dimensional and time-dependent data. In DPCA input matrix augmented by adding time-lagged values of variables. building model analyst needs to decide (1) number lags add, (2) given specific lag structure, how many components retain. this...

In recent years, there have been studies focusing on the use of different types autoencoders (AEs) for monitoring complex nonlinear data coming from industrial and chemical processes. However, in many cases focus was placed detection. As a result, practitioners are encountering problems trying to interpret such models obtaining candidate variables root cause analysis once an alarm is raised. This paper proposes novel statistical process control (SPC) framework based orthogonal (OAEs). OAEs...

With the development of data acquisition technologies, huge amounts data, which are apt to be modeled as functional now generated. In this setting, standard profile monitoring methods aim assess stability over time a completely observed quality characteristic. However, in some practical situations, assessing presence assignable causes is great interest even when characteristic not yet, that is, monitor process state realtime. To aim, we propose new method, referred real-time (FRTM), able...

With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in leveraging vast amounts data collected (bio)chemical processes to improve their operations. The objective this article provide perspective current status Big-Data-based process control methodologies most effective path further embed these processes. Therefore, provides an overview operational requirements, availability nature data, role structure hierarchy how they constrain endeavor. state seemingly...

Injection molding is one of the most important processes for mass production plastic parts. In recent years, many researchers have focused on predicting occurrence and intensity defects in injected molded parts, as well optimization process parameters to avoid such defects. One frequent manufactured parts blush, which usually occurs around gate location. this study, identify effective blush formation, eight design with effect probability influence defect been investigated. Using a...

One of the basic assumptions for traditional univariate and multivariate control charts is that data are independent in time. For latter, many cases, serially dependent (autocorrelated) cross‐correlated because of, example, frequent sampling process dynamics. It well known autocorrelation affects false alarm rate shift‐detection ability charts. However, how Hotelling T 2 chart affected by various cross‐correlation structures different magnitudes shifts mean not fully explored literature. In...

A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in statistical process control (SPC), is that the data are independent time. In many industrial processes, frequent sampling and dynamics make this unrealistic rendering sampled autocorrelated (serially dependent). PCA can be used to reduce dimensionality simplify multivariate SPC. Although there have been some attempts literature deal with PCA, we argue impact of autocorrelation on PCA-based...

Abstract In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or use expensive testing equipment. these circumstances, active learning can be highly beneficial in suggesting most informative data points to used when fitting a model. Reducing number needed for model development alleviates both computational burden required training and operational expenses related labeling. Online learning, particular,...

In industrial settings, collecting labeled data, i.e., input data with the corresponding output, is often expensive and time-consuming, while unlabeled process typically readily available in large quantities. This Quality Quandaries explores Semi-Supervised Learning (SSL) as a method to enhance predictive modeling by utilizing both data. We provide general discussion of SSL methods their applicability environments, where efficient utilization critical. To demonstrate practical utility SSL,...

The origins of quality engineering are in manufacturing, where engineers apply basic statistical methodologies to improve the and productivity products processes. In past decade, people have discovered that these effective for improving almost any type system or process, such as financial, health care, supply chains. This paper begins with a review key advances trends within over decade. second part uses first foundation outline new application areas field. It also discusses how needs evolve...

Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical (SPC) design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these are instrumental research education. This article focuses on the revised Tennessee Eastman simulator providing guidelines for its use as a testbed SPC DoE methods. We provide flowcharts that can support new users get started Simulink/Matlab...

AbstractPrincipal component analysis (PCA) has been a commonly used unsupervised learning method with broad applications in both descriptive and inferential analytics. It is widely for representation to extract key features from dataset visualize them lower dimensional space. With more of neural network-based methods, autoencoders (AEs) have gained popularity dimensionality reduction tasks. In this paper, we explore the intriguing relationship between PCA AEs demonstrate, through some...

We present an overview of predictive maintenance (PdM) in industrial operations, highlighting its evolution, benefits, challenges, and potential economic impact. It is assessed that there are considerable benefits associated with PdM but also practical implementation can prove difficult due to the uncertain application, need for advanced IT infrastructure as well expert personnel, and, perhaps most importantly, a lack failure-related data model training. conclude three case studies elaborate...